… a few items out of the whole in a recommender system used by Amazon or Taobao [4], [5].
In … Nonnegative Latent Factor model. NeuMF Neural Matrix Factorization model [75]. DCCR …

… High-dimensional and sparse matrices (HiDS) matrices with non-negativity constraints are …
sparse non-negative matrix factorization model for largescale recommender systems on GPUs…

… In many industrial applications related to big data, a so-called high-dimensional sparse matrix
is often used to represent relationships between entities, where the sparsity mainly results …

… Li, “Efficient extraction of non-negative latent factors from high-dimensional and sparse
matrices … Zhu, “A nonegative latent factor model for large-scale sparse matrices in recommender …

… Therefore, more and more researchers have shown their interests in Recommender
Systems (RSs) [1-5], which play an important role in assisting people to obtain their needed …

… of the proposed SNbased model in building CF-based recommender systems, its accuracy
… A Nonnegative latent factor model for large-Scale sparse matrices in recommender systems …

… matrix between the input layer and the hidden layer. Thus, in an FAE-based model, we can
map the input vector r i to the latent factor … Note that for a large-scale optimization problem, …

… non-negative matrix factorization (DNNMF) technique to address the above problem. First,
we impose non-negative … We design a nonlinear non-negative matrix factorization using deep …

… However, an LFA model relies heavily on its hyper… latent factor analysis (HLFA) model. Its
main idea is to build a swarm by taking the hyper-parameters of every single LFA-based model …

… application in large-scale recommender systems, the … In recommender systems, because
many users have sparse … uk denotes the preference of user u on the latent factor k ( k ∈ { 1 , 2 , …